Comprehensive Causal Machine Learning

TL;DR

This paper compares three comprehensive machine learning methods for causal effect estimation across different levels of granularity, highlighting their theoretical properties, strengths, and practical advantages in various scenarios.

Contribution

It provides a comparative analysis of mcf, grf, and dml approaches, including proven guarantees for mcf and insights into their performance under different conditions.

Findings

dml excels for population and group average effects with few groups

outcome-centred forests outperform for detailed causal heterogeneity

mcf is most robust when selection into treatment is strong and offers internal consistency

Abstract

Uncovering causal effects in multiple treatment setting at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to causal effect estimation allow to use a single causal machine learning approach for estimation and inference of causal mean effects for all levels of granularity. Focusing on selection-on-observables, this paper compares three such approaches, the modified causal forest (mcf), the generalized random forest (grf), and double machine learning (dml). It also compares the theoretical properties of the approaches and provides proven theoretical guarantees for the mcf. The findings indicate that dml-based methods excel for average treatment effects at the population level (ATE) and group level (GATE) with few groups, when selection into treatment is not too strong. However, for finer causal heterogeneity, explicitly outcome-centred forest-based approaches are superior. The mcf has three additional benefits: (i) It is the most robust estimator in cases when dml-based approaches underperform because of substantial selection into treatment; (ii) it is the best estimator for GATEs when the number of groups gets larger; and (iii), it is the only estimator that is internally consistent, in the sense that low-dimensional causal ATEs and GATEs are obtained as aggregates of finer-grained causal parameters.